Prompt Tracing for LangChain in Python

An example Python app demonstrating how to integrate Pangea's Secure Audit Log service into a LangChain app to maintain an audit log of prompts being sent to LLMs.

Prerequisites

• Python v3.12 or greater.
• pip v24.2 or uv v0.4.29.
• A Pangea account with Secure Audit Log enabled with the AI Audit Log Schema Config.
• An OpenAI API key.

Setup

git clone https://github.com/pangeacyber/langchain-python-prompt-tracing.git
cd langchain-python-prompt-tracing

If using pip:

python -m venv .venv
source .venv/bin/activate
pip install .

Or, if using uv:

uv sync
source .venv/bin/activate

The sample can then be executed with:

python -m langchain_prompt_tracing "Give me information on John Smith"

Usage

Usage: python -m langchain_prompt_tracing [OPTIONS] PROMPT

Options:
  --model TEXT             OpenAI model.  [default: gpt-4o-mini; required]
  --audit-token SECRET     Pangea Secure Audit Log API token. May also be set
                           via the `PANGEA_AUDIT_TOKEN` environment variable.
                           [required]
  --audit-config-id TEXT   Pangea Secure Audit Log configuration ID.
  --pangea-domain TEXT     Pangea API domain. May also be set via the
                           `PANGEA_DOMAIN` environment variable.  [default:
                           aws.us.pangea.cloud; required]
  --openai-api-key SECRET  OpenAI API key. May also be set via the
                           `OPENAI_API_KEY` environment variable.  [required]
  --help                   Show this message and exit.

Example Input/Output

This does not modify the input or output so it's transparent to the LLM and end user.

Audit logs can be viewed at the Secure Audit Log Viewer.